Intro to R and COVID-19 EDA
Julia Layne
6/29/2020
Resources for Learning R
DataCamp Intro To R
- Truly introductory
- Code In Browser, has hints
Install R
- I grabbed the Texas Mirror, but any will work on this page
- R Install Mirrors
RStudio Cloud
- Allows you to use R before installing. You can work on projects from any computer with a browser.
- Out of Beta August 2020 - Plans and Pricing
FreeCodeCamp Intro Video
- Made by Barton Poulson
- His R Intro
- Intro to R Playlist, I suggest starting at the plot. You can go back to the previous
R Basics
This is an R Markdown file. It lets you run by code block or output a report with knittr
Math
print("1 + 1")## [1] "1 + 1"1 + 1## [1] 2print("13* 13")## [1] "13* 13"13* 13## [1] 169print("25 / 5")## [1] "25 / 5"25 / 5## [1] 5print("21 / 5")## [1] "21 / 5"21 / 5## [1] 4.2print("21 %% 5")## [1] "21 %% 5"21 %% 5## [1] 1print("5^3")## [1] "5^3"5^3## [1] 125Variables
5 -> five
thirteen = 13
print("1 + 1")## [1] "1 + 1"1 + 1## [1] 2print("13* 13")## [1] "13* 13"thirteen* thirteen## [1] 169print("25 / 5")## [1] "25 / 5"(20 + five) / five## [1] 5print("21 / 5")## [1] "21 / 5"21 / five## [1] 4.2print("21 %% 5")## [1] "21 %% 5"21 %% five## [1] 1print("5^3")## [1] "5^3"five^3## [1] 125Vectors
names <- c('test','Myocarditis','beta coronavirus','novel corona 2019','Encephalitis','Hepatitis A', 'influenza', 'coronaitis')
names## [1] "test" "Myocarditis" "beta coronavirus"
## [4] "novel corona 2019" "Encephalitis" "Hepatitis A"
## [7] "influenza" "coronaitis"which_name <- function(name) {
type <- 'Neither'
if(grepl('corona', name,ignore.case = TRUE)){
type <-'corona'
}
if(grepl('itis', name,ignore.case = TRUE)){
type <-'itis'
}
type
}
#testing top dataset to confirm it is working
sapply(names, which_name,simplify = TRUE)## test Myocarditis beta coronavirus novel corona 2019
## "Neither" "itis" "corona" "corona"
## Encephalitis Hepatitis A influenza coronaitis
## "itis" "itis" "Neither" "itis"COVID DATASET
- From the John Hopkins dataset avaiable on GitHub
- COVID-19/csse_covid_19_data/csse_covid_19_daily_reports/
Read in Files
Change with new COVID file for most up to date info
covid_raw = read.csv("06-28-2020-us.csv",header = TRUE)
#covid_ts_confirmed = read.csv(file.choose(),header = TRUE)Look at Raw Data
head(covid_raw)## Province_State Country_Region Last_Update Lat Long_
## 1 Alabama US 2020-06-29 04:33:57 32.3182 -86.9023
## 2 Alaska US 2020-06-29 04:33:57 61.3707 -152.4044
## 3 American Samoa US 2020-06-29 04:33:57 -14.2710 -170.1320
## 4 Arizona US 2020-06-29 04:33:57 33.7298 -111.4312
## 5 Arkansas US 2020-06-29 04:33:57 34.9697 -92.3731
## 6 California US 2020-06-29 04:33:57 36.1162 -119.6816
## Confirmed Deaths Recovered Active FIPS Incident_Rate People_Tested
## 1 35441 919 18866 15656 1 722.8159 386280
## 2 880 14 521 345 2 120.2934 108300
## 3 0 0 NA 0 60 0.0000 696
## 4 73920 1594 8926 63400 4 1015.5636 509896
## 5 19818 264 13270 6284 5 656.7027 291222
## 6 215296 5932 NA 209364 6 544.8846 3955952
## People_Hospitalized Mortality_Rate UID ISO3 Testing_Rate
## 1 2703 2.593042 84000001 USA 7878.145
## 2 NA 1.590909 84000002 USA 14804.284
## 3 NA NA 16 ASM 1250.876
## 4 4617 2.156385 84000004 USA 7005.301
## 5 1373 1.332122 84000005 USA 9650.130
## 6 NA 2.755276 84000006 USA 10011.970
## Hospitalization_Rate
## 1 7.626760
## 2 NA
## 3 NA
## 4 6.245942
## 5 6.928045
## 6 NA#covid_ts_confirmedPlot Data
Initial lookinto ploting data points Note here
#install.packages("tidyverse")
library(tidyverse)## ── Attaching packages ────────────────────────────────────────────────────────────────────────────────── tidyverse 1.3.0 ──## ✓ ggplot2 3.3.0 ✓ purrr 0.3.4
## ✓ tibble 3.0.1 ✓ dplyr 1.0.0
## ✓ tidyr 1.1.0 ✓ stringr 1.4.0
## ✓ readr 1.3.1 ✓ forcats 0.5.0## ── Conflicts ───────────────────────────────────────────────────────────────────────────────────── tidyverse_conflicts() ──
## x dplyr::filter() masks stats::filter()
## x dplyr::lag() masks stats::lag()library(ggplot2)
library(GGally)## Registered S3 method overwritten by 'GGally':
## method from
## +.gg ggplot2library(plotly)##
## Attaching package: 'plotly'## The following object is masked from 'package:ggplot2':
##
## last_plot## The following object is masked from 'package:stats':
##
## filter## The following object is masked from 'package:graphics':
##
## layoutplot(covid_raw)
byState10 <- covid_raw %>% head(10) %>% ggplot(aes(x=Deaths, y=Confirmed, fill=Province_State)) + geom_point(aes(fill=Province_State)) + ggtitle("Deaths vs Confirmed Cases in First Ten Provinces")
ggplotly(byState10)byState <- covid_raw %>% ggplot(aes(x=Deaths, y=Confirmed, fill=Province_State)) + geom_point(aes(fill=Province_State)) + ggtitle("Deaths vs Confirmed Cases per Province")
ggplotly(byState)byStateNoNY <- filter(covid_raw, Province_State != 'New York' & Province_State != 'New Jersey') %>% ggplot(aes(x=Deaths, y=Confirmed, fill=Province_State)) + geom_point(aes(fill=Province_State)) + ggtitle("Deaths vs Confirmed Cases per Province")
ggplotly(byStateNoNY)#covid_raw %>% head(10) %>% ggplot(aes(x=Deaths, y=Confirmed, fill=Province_State) + geom_point(aes(fill=Province_State)) + ggtitle("Deaths vs Confirmed Cases in Top Countries (Minus China)")statesAbove700Deaths <- filter(covid_raw, Deaths >= 700) %>% ggplot(aes(x=Deaths, y=Confirmed, fill=Province_State)) + geom_point(aes(fill=Province_State)) + ggtitle("Over 700 Mortality States")
ggplotly(statesAbove700Deaths)statesAbove700Deaths <- filter(covid_raw, Deaths < 700) %>% ggplot(aes(x=Deaths, y=Confirmed, fill=Province_State)) + geom_point(aes(fill=Province_State)) + ggtitle("Under 700 Mortality States")
ggplotly(statesAbove700Deaths)Map data using Fips codes
library(e1071)
library(usmap)
UScovid_dataset <- filter(covid_raw, Country_Region == 'US' & FIPS != 'NA')
UScovid_dataset## Province_State Country_Region Last_Update Lat
## 1 Alabama US 2020-06-29 04:33:57 32.3182
## 2 Alaska US 2020-06-29 04:33:57 61.3707
## 3 American Samoa US 2020-06-29 04:33:57 -14.2710
## 4 Arizona US 2020-06-29 04:33:57 33.7298
## 5 Arkansas US 2020-06-29 04:33:57 34.9697
## 6 California US 2020-06-29 04:33:57 36.1162
## 7 Colorado US 2020-06-29 04:33:57 39.0598
## 8 Connecticut US 2020-06-29 04:33:57 41.5978
## 9 Delaware US 2020-06-29 04:33:57 39.3185
## 10 Diamond Princess US 2020-06-29 04:33:57 NA
## 11 District of Columbia US 2020-06-29 04:33:57 38.8974
## 12 Florida US 2020-06-29 04:33:57 27.7663
## 13 Georgia US 2020-06-29 04:33:57 33.0406
## 14 Grand Princess US 2020-06-29 04:33:57 NA
## 15 Guam US 2020-06-29 04:33:57 13.4443
## 16 Hawaii US 2020-06-29 04:33:57 21.0943
## 17 Idaho US 2020-06-29 04:33:57 44.2405
## 18 Illinois US 2020-06-29 04:33:57 40.3495
## 19 Indiana US 2020-06-29 04:33:57 39.8494
## 20 Iowa US 2020-06-29 04:33:57 42.0115
## 21 Kansas US 2020-06-29 04:33:57 38.5266
## 22 Kentucky US 2020-06-29 04:33:57 37.6681
## 23 Louisiana US 2020-06-29 04:33:57 31.1695
## 24 Maine US 2020-06-29 04:33:57 44.6939
## 25 Maryland US 2020-06-29 04:33:57 39.0639
## 26 Massachusetts US 2020-06-29 04:33:57 42.2302
## 27 Michigan US 2020-06-29 04:33:57 43.3266
## 28 Minnesota US 2020-06-29 04:33:57 45.6945
## 29 Mississippi US 2020-06-29 04:33:57 32.7416
## 30 Missouri US 2020-06-29 04:33:57 38.4561
## 31 Montana US 2020-06-29 04:33:57 46.9219
## 32 Nebraska US 2020-06-29 04:33:57 41.1254
## 33 Nevada US 2020-06-29 04:33:57 38.3135
## 34 New Hampshire US 2020-06-29 04:33:57 43.4525
## 35 New Jersey US 2020-06-29 04:33:57 40.2989
## 36 New Mexico US 2020-06-29 04:33:57 34.8405
## 37 New York US 2020-06-29 04:33:57 42.1657
## 38 North Carolina US 2020-06-29 04:33:57 35.6301
## 39 North Dakota US 2020-06-29 04:33:57 47.5289
## 40 Northern Mariana Islands US 2020-06-29 04:33:57 15.0979
## 41 Ohio US 2020-06-29 04:33:57 40.3888
## 42 Oklahoma US 2020-06-29 04:33:57 35.5653
## 43 Oregon US 2020-06-29 04:33:57 44.5720
## 44 Pennsylvania US 2020-06-29 04:33:57 40.5908
## 45 Puerto Rico US 2020-06-29 04:33:57 18.2208
## 46 Rhode Island US 2020-06-29 04:33:57 41.6809
## 47 South Carolina US 2020-06-29 04:33:57 33.8569
## 48 South Dakota US 2020-06-29 04:33:57 44.2998
## 49 Tennessee US 2020-06-29 04:33:57 35.7478
## 50 Texas US 2020-06-29 04:33:57 31.0545
## 51 Utah US 2020-06-29 04:33:57 40.1500
## 52 Vermont US 2020-06-29 04:33:57 44.0459
## 53 Virgin Islands US 2020-06-29 04:33:57 18.3358
## 54 Virginia US 2020-06-29 04:33:57 37.7693
## 55 Washington US 2020-06-29 04:33:57 47.4009
## 56 West Virginia US 2020-06-29 04:33:57 38.4912
## 57 Wisconsin US 2020-06-29 04:33:57 44.2685
## 58 Wyoming US 2020-06-29 04:33:57 42.7560
## Long_ Confirmed Deaths Recovered Active FIPS Incident_Rate
## 1 -86.9023 35441 919 18866 15656 1 722.81588
## 2 -152.4044 880 14 521 345 2 120.29335
## 3 -170.1320 0 0 NA 0 60 0.00000
## 4 -111.4312 73920 1594 8926 63400 4 1015.56359
## 5 -92.3731 19818 264 13270 6284 5 656.70269
## 6 -119.6816 215296 5932 NA 209364 6 544.88455
## 7 -105.3111 32290 1676 4442 26172 8 560.71332
## 8 -72.7554 46303 4316 8053 33934 9 1298.71733
## 9 -75.5071 11226 507 6665 4054 10 1152.84607
## 10 NA 49 0 NA 49 88888 NA
## 11 -77.0268 10248 550 1199 8499 11 1452.07432
## 12 -81.6868 141075 3419 NA 137656 12 656.84294
## 13 -83.6431 77210 2778 NA 74432 13 727.20094
## 14 NA 103 3 NA 100 99999 NA
## 15 144.7937 247 5 179 63 66 150.39975
## 16 -157.4983 899 18 714 167 15 63.49444
## 17 -114.4788 5322 91 3898 1333 16 297.80674
## 18 -88.9861 141723 6888 NA 134835 17 1118.41068
## 19 -86.2583 44930 2619 33935 8376 18 667.38768
## 20 -93.2105 28520 706 17620 10194 19 903.94191
## 21 -96.7265 13847 269 779 12799 20 475.30064
## 22 -84.6701 15232 558 3730 10944 21 340.93811
## 23 -91.8678 56236 3199 39792 13245 22 1209.69008
## 24 -69.3819 3191 104 2577 510 23 237.38815
## 25 -76.8021 66777 3168 4976 58633 24 1104.54076
## 26 -71.5301 108667 8059 NA 100608 25 1576.59706
## 27 -84.5361 69946 6157 51099 12690 26 700.38051
## 28 -93.9002 35549 1460 30809 3280 27 630.34255
## 29 -89.6787 25892 1039 17242 7611 28 869.98332
## 30 -92.2884 20689 1004 NA 19685 29 337.09560
## 31 -110.4544 863 22 604 237 30 80.74642
## 32 -98.2681 18899 267 13053 5579 31 976.99141
## 33 -117.0554 17160 500 684 15976 32 557.11464
## 34 -71.5639 5747 367 4401 979 33 422.66335
## 35 -74.5210 171182 14975 30092 126115 34 1927.24992
## 36 -106.2485 11809 492 5251 6066 35 563.18374
## 37 -74.9481 392539 31397 70010 291132 36 2017.82594
## 38 -79.8064 62248 1352 36921 23975 37 593.51165
## 39 -99.7840 3495 79 3139 277 38 458.62410
## 40 145.6739 30 2 19 9 69 54.40302
## 41 -82.7649 50309 2807 NA 47502 39 430.39242
## 42 -96.9289 12947 385 9397 3165 40 327.19472
## 43 -122.0709 8341 202 2649 5490 41 197.76008
## 44 -77.2098 89863 6606 66686 16571 42 701.94561
## 45 -66.5901 7189 153 NA 7036 72 245.07331
## 46 -71.5118 16661 927 1600 14134 44 1572.74055
## 47 -80.9450 33320 716 13456 19148 45 647.15189
## 48 -99.4388 6681 91 5752 838 46 755.20624
## 49 -86.6923 40172 584 26159 13429 47 588.24098
## 50 -97.5635 150152 2402 79974 67776 48 517.83907
## 51 -111.8624 21100 167 11931 9002 49 658.14961
## 52 -72.7107 1202 56 946 200 50 192.63160
## 53 -64.8963 81 6 71 4 78 75.51180
## 54 -78.1700 61736 1732 8005 51999 51 723.28349
## 55 -121.4905 31752 1310 NA 30442 53 416.97237
## 56 -80.9545 2832 93 2062 677 54 158.02275
## 57 -89.6165 27743 777 21953 5013 55 476.48458
## 58 -107.3025 1417 20 1057 340 56 244.83421
## People_Tested People_Hospitalized Mortality_Rate UID ISO3 Testing_Rate
## 1 386280 2703 2.5930420 84000001 USA 7878.1445
## 2 108300 NA 1.5909091 84000002 USA 14804.2841
## 3 696 NA NA 16 ASM 1250.8762
## 4 509896 4617 2.1563853 84000004 USA 7005.3005
## 5 291222 1373 1.3321223 84000005 USA 9650.1297
## 6 3955952 NA 2.7552765 84000006 USA 10011.9702
## 7 313711 5399 5.1904614 84000008 USA 5447.5670
## 8 438623 10268 9.3212103 84000009 USA 12302.6000
## 9 106346 NA 4.5163014 84000010 USA 10921.1267
## 10 NA NA 0.0000000 84088888 USA NA
## 11 93132 NA 5.3669009 84000011 USA 13196.1930
## 12 1881897 14540 2.4235336 84000012 USA 8762.0823
## 13 806938 10711 3.5979795 84000013 USA 7600.1305
## 14 NA NA 2.9126214 84099999 USA NA
## 15 12378 NA 2.0242915 316 GUM 7537.0367
## 16 75478 110 2.0022247 84000015 USA 5330.8491
## 17 86345 312 1.7098835 84000016 USA 4831.6653
## 18 1546031 NA 4.8601850 84000017 USA 12200.5432
## 19 470535 7003 5.8290674 84000018 USA 6989.3003
## 20 295915 NA 2.4754558 84000019 USA 9379.0312
## 21 167859 1128 1.9426591 84000020 USA 5761.7888
## 22 358491 2590 3.6633403 84000021 USA 8024.1101
## 23 696111 NA 5.6885269 84000022 USA 14974.0126
## 24 93495 346 3.2591664 84000023 USA 6955.3761
## 25 519473 10793 4.7441484 84000024 USA 8592.4660
## 26 835794 11319 7.4162349 84000025 USA 12126.1318
## 27 1016679 NA 8.8025048 84000026 USA 10180.1698
## 28 585417 4010 4.1070072 84000027 USA 10380.4113
## 29 280188 3102 4.0128225 84000028 USA 9414.4480
## 30 361173 NA 4.8528203 84000029 USA 5884.7615
## 31 82474 97 2.5492468 84000030 USA 7716.6633
## 32 172798 1315 1.4127732 84000031 USA 8932.8621
## 33 267580 NA 2.9137529 84000032 USA 8687.2223
## 34 116109 562 6.3859405 84000033 USA 8539.2411
## 35 1387833 19841 8.7479992 84000034 USA 15624.8966
## 36 322959 1851 4.1663138 84000035 USA 15402.2574
## 37 3816485 89995 7.9984409 84000036 USA 19618.4390
## 38 871905 NA 2.1719573 84000037 USA 8313.2915
## 39 103925 226 2.2603720 84000038 USA 13637.3418
## 40 8217 NA 6.6666667 580 MNP 14900.9865
## 41 756765 7681 5.5795186 84000039 USA 6474.1084
## 42 326015 1456 2.9736619 84000040 USA 8239.0040
## 43 217391 1022 2.4217720 84000041 USA 5154.2095
## 44 742982 NA 7.3511901 84000042 USA 5803.6450
## 45 7189 NA 2.1282515 630 PRI 245.0733
## 46 230508 1984 5.5638917 84000044 USA 21759.1548
## 47 359802 2622 2.1488595 84000045 USA 6988.1916
## 48 78893 652 1.3620715 84000046 USA 8917.8994
## 49 748553 2564 1.4537489 84000047 USA 10961.1060
## 50 1775219 NA 1.5997123 84000048 USA 6122.3144
## 51 328449 1396 0.7914692 84000049 USA 10244.9564
## 52 63865 NA 4.6589018 84000050 USA 10234.9561
## 53 2827 NA 7.4074074 850 VIR 2635.4551
## 54 628328 8823 2.8054944 84000051 USA 7361.3333
## 55 525802 4240 4.1257244 84000053 USA 6904.9165
## 56 166508 NA 3.2838983 84000054 USA 9290.9789
## 57 552454 3393 2.8007065 84000055 USA 9488.3686
## 58 31823 112 1.4114326 84000056 USA 5498.4890
## Hospitalization_Rate
## 1 7.626760
## 2 NA
## 3 NA
## 4 6.245942
## 5 6.928045
## 6 NA
## 7 16.720347
## 8 22.175669
## 9 NA
## 10 NA
## 11 NA
## 12 10.306575
## 13 13.872555
## 14 NA
## 15 NA
## 16 12.235818
## 17 5.862458
## 18 NA
## 19 15.586468
## 20 NA
## 21 8.146169
## 22 17.003676
## 23 NA
## 24 10.842996
## 25 16.162751
## 26 10.416226
## 27 NA
## 28 11.280205
## 29 11.980535
## 30 NA
## 31 11.239861
## 32 6.958040
## 33 NA
## 34 9.779015
## 35 11.590588
## 36 15.674486
## 37 22.926384
## 38 NA
## 39 6.466381
## 40 NA
## 41 15.267646
## 42 11.245848
## 43 12.252727
## 44 NA
## 45 NA
## 46 11.908049
## 47 7.869148
## 48 9.759018
## 49 6.382555
## 50 NA
## 51 6.616114
## 52 NA
## 53 NA
## 54 14.291499
## 55 13.353490
## 56 NA
## 57 12.230112
## 58 7.904023#UScovid_dataset$fips <- fips(brew_count_by_state$state)
attach(UScovid_dataset)
UScovid_dataset_fips <- UScovid_dataset[order(FIPS),]
detach(UScovid_dataset)
UScovid_dataset_fips$fips = UScovid_dataset_fips$FIPS
plot_usmap(data = UScovid_dataset_fips,
values = "Deaths",
color = rgb(.2, .7, 1)) +
labs(title = "Covid Deaths by State",
subtitle = "Count of Covid19 Deaths per state") +
scale_fill_continuous(low = "white", high = rgb(.2, .7, 1),
name = "Deaths per state", label = scales::comma) + theme(legend.position = "right")## Warning: Use of `map_df$x` is discouraged. Use `x` instead.## Warning: Use of `map_df$y` is discouraged. Use `y` instead.## Warning: Use of `map_df$group` is discouraged. Use `group` instead.
plot_usmap(data = filter(UScovid_dataset_fips, Province_State != 'New York'), values = "Deaths", color = rgb(.2, .7, 1)) +
labs(title = "Covid Deaths by State (New York Removed)", subtitle = "Count of Covid19 Deaths per state") +
scale_fill_continuous(low = "white", high = rgb(.2, .7, 1), name = "Deaths per state", label = scales::comma) + theme(legend.position = "right")## Warning: Use of `map_df$x` is discouraged. Use `x` instead.## Warning: Use of `map_df$y` is discouraged. Use `y` instead.## Warning: Use of `map_df$group` is discouraged. Use `group` instead.
plot_usmap(data = filter(UScovid_dataset_fips, Province_State != 'New York' & Province_State != 'New Jersey'), values = "Deaths", color = rgb(.2, .7, 1)) +
labs(title = "Covid Deaths by State (New York Removed)", subtitle = "Count of Covid19 Deaths per state") +
scale_fill_continuous(low = "white", high = rgb(.2, .7, 1), name = "Deaths per state", label = scales::comma) + theme(legend.position = "right")## Warning: Use of `map_df$x` is discouraged. Use `x` instead.## Warning: Use of `map_df$y` is discouraged. Use `y` instead.## Warning: Use of `map_df$group` is discouraged. Use `group` instead.
Previous Work - Finding the top Countries by Confirmed Cases
Read in Files
Change with new COVID file for most up to date info
covid_raw_world = read.csv("06-28-2020.csv",header = TRUE)
#covid_ts_confirmed = read.csv(file.choose(),header = TRUE)
head(covid_raw_world)## FIPS Admin2 Province_State Country_Region Last_Update Lat
## 1 45001 Abbeville South Carolina US 2020-06-29 04:33:44 34.22333
## 2 22001 Acadia Louisiana US 2020-06-29 04:33:44 30.29506
## 3 51001 Accomack Virginia US 2020-06-29 04:33:44 37.76707
## 4 16001 Ada Idaho US 2020-06-29 04:33:44 43.45266
## 5 19001 Adair Iowa US 2020-06-29 04:33:44 41.33076
## 6 21001 Adair Kentucky US 2020-06-29 04:33:44 37.10460
## Long_ Confirmed Deaths Recovered Active Combined_Key
## 1 -82.46171 103 0 0 103 Abbeville, South Carolina, US
## 2 -92.41420 812 36 0 776 Acadia, Louisiana, US
## 3 -75.63235 1039 14 0 1025 Accomack, Virginia, US
## 4 -116.24155 1841 23 0 1818 Ada, Idaho, US
## 5 -94.47106 15 0 0 15 Adair, Iowa, US
## 6 -85.28130 111 19 0 92 Adair, Kentucky, US
## Incidence_Rate Case.Fatality_Ratio
## 1 419.9454 0.000000
## 2 1308.7275 4.433498
## 3 3215.1256 1.347449
## 4 382.2778 1.249321
## 5 209.7315 0.000000
## 6 578.0648 17.117117confirmed_by_country <- covid_raw_world%>% group_by(Country_Region) %>% tally(Confirmed, name = "Confirmed", sort = TRUE)
head(confirmed_by_country)## # A tibble: 6 x 2
## Country_Region Confirmed
## <chr> <int>
## 1 US 2548996
## 2 Brazil 1344143
## 3 Russia 633563
## 4 India 548318
## 5 United Kingdom 312640
## 6 Peru 279419deaths_by_country <- covid_raw_world%>% group_by(Country_Region) %>% tally(Deaths, name = "Deaths", sort = TRUE)
head(deaths_by_country)## # A tibble: 6 x 2
## Country_Region Deaths
## <chr> <int>
## 1 US 125803
## 2 Brazil 57622
## 3 United Kingdom 43634
## 4 Italy 34738
## 5 France 29781
## 6 Spain 28343totals <- merge(confirmed_by_country, deaths_by_country, by="Country_Region")
head(totals)## Country_Region Confirmed Deaths
## 1 Afghanistan 30967 721
## 2 Albania 2402 55
## 3 Algeria 13273 897
## 4 Andorra 855 52
## 5 Angola 267 11
## 6 Antigua and Barbuda 69 3Then reordered by Confirmed
#order(totals$Confirmed, decreasing = TRUE)
#totals$Confirmed
#totals[180,]
top_to_least <- totals[order(totals$Confirmed, decreasing = TRUE),]
head(top_to_least)## Country_Region Confirmed Deaths
## 180 US 2548996 125803
## 24 Brazil 1344143 57622
## 141 Russia 633563 9060
## 80 India 548318 16475
## 178 United Kingdom 312640 43634
## 135 Peru 279419 9317top10Confirmed <- top_to_least %>% head(10) %>% ggplot(aes(x=Deaths, y=Confirmed, fill=Country_Region)) + geom_point(aes(fill=Country_Region)) + ggtitle("Deaths vs Confirmed Cases in Top countries")
ggplotly(top10Confirmed)# At the time, China was the highest and I wanted to look at the rest, now it is much different
top10ConfirmedMinusUS <- subset(top_to_least, Country_Region != "US") %>% head(10) %>% ggplot(aes(x=Deaths, y=Confirmed, fill=Country_Region)) + geom_point(aes(fill=Country_Region)) + ggtitle("Deaths vs Confirmed Cases in Top Countries (Minus US)")
ggplotly(top10ConfirmedMinusUS)# Now removing US instead
top10ConfirmedMinusUSandB <- subset(top_to_least, Country_Region != "US" & Country_Region != "Brazil") %>% head(10) %>% ggplot(aes(x=Deaths, y=Confirmed, fill=Country_Region)) + geom_point(aes(fill=Country_Region)) + ggtitle("Deaths vs Confirmed Cases in Top Countries (Minus US and Brazil)")
ggplotly(top10ConfirmedMinusUSandB)